Method
Editorial log
Every published page on this site, who reviewed it, when, and what the review changed.
Scaled content is penalised when it is published without review. Rather than assert that review happens, the record is kept here and the build will not publish a page whose review block is missing or dated before the page was written.
One collection is reviewed differently and the table says so rather than hiding it. Market pages carry no third-party figure — every publishable fact on one is a named authority, a section number, an institution or a link — so each is checked by retrieving the source and searching it, and the URL, the status, the hash and the matched passage are recorded. A field that cannot be established that way is dropped; a page whose required fields cannot be is not published. Everything that carries a figure somebody else measured is still read by a person before it ships.
| Page | Type | Reviewed by | Reviewed | What the review changed |
|---|---|---|---|---|
| What happens when an AI tool causes a HIPAA breach? | Answer | Answer Production Engine | 2026-08-26 | Cut penalty tiers and enforcement figures. They are adjusted periodically, they were not what a practice can act on, and they crowded out the scoping argument, which is the only part of this page that changes an outcome. Added the notification-letter paragraph after review noted the page stopped at the point the reader's real problem begins. |
| What may an AI agent do with MLS listing data? | Answer | Answer Production Engine | 2026-08-26 | Removed specific clause numbers from one association's policy. Numbering differs between markets and quoting one set would have read as universal, which is the error the closing paragraph warns against. Replaced with the categories of term to look for, which are stable across agreements. |
| What should an engagement letter say about AI? | Answer | Answer Production Engine | 2026-08-26 | Deleted a model clause. Publishing drafting language would have produced exactly the boilerplate the opinion says is inadequate, with this page's authority behind it. Replaced it with the list of specifics a clause has to contain, which a firm has to apply to its own arrangement to use at all. |
| Can you trust AI to summarise something you have not read? | Answer | Answer Production Engine | 2026-08-25 | Added the ask-what-it-omitted technique after establishing it produces a genuinely different pass over the material. Kept the obligation-document case as a firm limit rather than a caution, since that is where summaries are most wanted and least adequate, and softening it would have been the more comfortable and less accurate position. |
| Do automated text messages have different rules by state? | Answer | Answer Production Engine | 2026-08-25 | Added the mixed-content failure, which is the specific mechanism by which businesses with good intentions end up outside the rules, and which no discussion of consent addresses. Removed the enumeration of which states have added provisions, since that list changes each legislative session and the structural point does not. |
| Do multi-agent systems actually work better? | Answer | Answer Production Engine | 2026-08-25 | The draft answered yes with caveats. Reversed to a qualified no after separating the throughput claim from the quality claim, which are usually argued together and have different answers. Added the debugging cost, which is the expense teams discover after building and which no amount of design attention removes. |
| Do you have to tell customers they are talking to AI? | Answer | Answer Production Engine | 2026-08-25 | Checked the trigger conditions rather than describing a general disclosure duty, and they differ enough between states that a single rule would have been wrong somewhere. Removed the penalty amounts, which vary and date. Added the operational argument for disclosing anyway, which is the part that makes the answer actionable regardless of jurisdiction. |
| Does a bigger context window solve the memory problem? | Answer | Answer Production Engine | 2026-08-25 | Removed named window sizes from the draft. They date within months and the argument does not depend on any figure. Added the countervailing accuracy point, without which the page would read as a purely definitional correction rather than a reason to be cautious about filling the space that arrives. |
| Does where your business operates change which AI you can use? | Answer | Answer Production Engine | 2026-08-25 | Added the sectoral overlay, because a business in a regulated sector reading a page about geographic variation will reach the wrong conclusion: their binding constraint is not location and applies everywhere. Also replaced a survey of current statutes with the four areas, since the survey would date and the areas will not. |
| How do state call recording laws change what you can automate? | Answer | Answer Production Engine | 2026-08-25 | Cut a list of which states require all-party consent. Such lists date as statutes are amended and invite a reader to rely on a page rather than on the statute, and the structural point about which rule governs an interstate call does the work without the list. Added the recording-free alternatives, which the draft omitted. |
| How do you ask AI a question that gets a useful answer? | Answer | Answer Production Engine | 2026-08-25 | Cut a list of prompt patterns, which is what every treatment of this question supplies and which teaches copying a shape rather than noticing what is missing. Added the iterate-on-the-question point, which is the habit that changes outcomes most and which the correction reflex actively prevents. |
| How can a small business use AI without hiring an AI specialist? | Answer | Answer Production Engine | 2026-08-25 | Added the direct-use route, which the draft skipped entirely by treating the question as being about systems. Most of the available value in a small business needs no integration, and a page that goes straight to building recommends a maintenance burden before the free option has been used. |
| How do you build an evaluation set for an AI workflow? | Answer | Answer Production Engine | 2026-08-25 | Added the representativeness warning, which follows directly from the growth recommendation and undermines it if unaddressed: a set fed only failures stops measuring production. Also added the labelling disagreement observation, which is consistently the most useful by-product and was not in the draft. |
| How do you break a large task into agent-sized tasks? | Answer | Answer Production Engine | 2026-08-25 | Cut a recommended piece size from the draft. Any duration or step count quoted as a target would be copied without the handover criterion that actually governs, and the criterion is the answer. Added the cost-of-division paragraph, since the page otherwise argued in one direction only and over-division is a real and common failure. |
| How do you calculate whether an AI automation is worth building? | Answer | Answer Production Engine | 2026-08-25 | Added consistency as an explicit term after noticing the draft's arithmetic would reject automations whose value is that they never get skipped, which is a large share of the useful ones in a small business. Also added the measurement point about inflated duration estimates, which usually moves the answer in the opposite direction. |
| How do you check whether an AI answer is correct? | Answer | Answer Production Engine | 2026-08-25 | Removed a checklist that included re-reading the output for coherence. Coherence is what the system is best at producing and its presence carries no information about accuracy, so the checklist would have directed effort at the least informative signal. Added the consensus paragraph, which was missing and is the case where careful checking actively confirms an error. |
| How do you connect AI to the tools you already use? | Answer | Answer Production Engine | 2026-08-25 | Cut a list of named integration products. Naming them dates the page and would make it a directory rather than a decision aid, and the three routes are stable while the products are not. Promoted the manual route to first position after checking how many of the jobs readers describe actually need a standing grant. |
| How do you debug a multi-agent system? | Answer | Answer Production Engine | 2026-08-25 | Added the warning against adding a reviewer to fix a debugging problem, which is the natural response and makes the system harder to trace. Also corrected the draft's assumption that the failing participant is the one to fix; replaying briefs in isolation showed the ambiguity usually sits one level up. |
| How do you detect hallucinations at scale? | Answer | Answer Production Engine | 2026-08-25 | Cut a proposed detection score combining several signals into one number. A composite hides which signal moved, which is the only thing worth knowing when it changes. Added the downstream feedback path, which is free, already exists in every business, and was missing from the draft entirely. |
| How do you evaluate an AI workflow? | Answer | Answer Production Engine | 2026-08-25 | Added coverage and cost per item as required companions to accuracy. The draft measured accuracy alone, which is blind to silent skipping and lags the earliest signal of drift, so a reader following it would have concluded a degrading workflow was healthy. Cut a suggested target accuracy figure. |
| How do you get a coding agent to understand a large codebase? | Answer | Answer Production Engine | 2026-08-25 | The draft accepted the question's premise and described how to summarise a codebase into context. That approach produces a stale description that competes with the code, so the page now rejects the premise and argues for navigation. Removed a recommended documentation structure for the same reason. |
| How do you get AI to disagree with you? | Answer | Answer Production Engine | 2026-08-25 | Added the accommodation-after-pushback point, which the draft omitted and which defeats every other technique on the page: a reader who applies the reframing and then argues with the result has undone the work. Also added the general-category test for distinguishing produced objections from real ones. |
| How do you get an AI to remember what it needs to know about a project? | Answer | Answer Production Engine | 2026-08-25 | The draft recommended recording project structure in the instructions file. That is the exact content that drifts while continuing to be believed, so the section was replaced with the discoverability test. Added the rot paragraph after noting that no part of the draft addressed a written decision being quietly reversed. |
| How do you get consistent output from AI? | Answer | Answer Production Engine | 2026-08-25 | Added the input-variation diagnostic, which the draft omitted and which is the most common actual cause of what people report as inconsistency. Also added the closing caution, since the controls applied to a drafting task produce uniform output that no longer fits the situation, which is a loss the page would otherwise recommend. |
| How do you give an agent a budget and a deadline? | Answer | Answer Production Engine | 2026-08-25 | Added the soft threshold, which the draft lacked: a hard cap alone produces a run terminated mid-operation with nothing written, which is a worse outcome than the overrun it prevented. Also separated per-period from per-run limits, since over-triggering is the cost failure that a per-run cap does nothing about. |
What the build refuses to publish.
Four checks run before any page in this system is generated, and each one stops the build rather than producing a warning nobody reads.
A page must name, in writing, what is on it that could not appear unchanged on a sibling page — and no two pages may give the same answer. Independently of that, every page is reduced to a fingerprint with its own subject and place names masked out; if two fingerprints match, or come close, both pages are named and the build stops. That is the check that catches a page whose only distinguishing feature was the city in the heading.
A page must also carry at least two datapoints that are specific to its own subject rather than to its category, and if the same figure is claimed as specific on two different pages, it was specific to neither. Pages ship in cohorts with a size cap so indexation can be observed between them, and every page carries the review block that produces the table above.
All four are properties of the schema, not of anyone remembering. Reviewed by Siddharth Sharma, except the 0 market page(s) above, which are checked against their sources and carry the record of it.
GATES
A gate enforced by a person reading pages stops being enforced somewhere around page 60.